Enzyme Synergy in Transient Clusters of Endo- and Exocellulase Enables a Multilayer Mode of Processive Depolymerization of Cellulose

Biological degradation of cellulosic materials relies on the molecular-mechanistic principle that internally chain-cleaving endocellulases work synergistically with chain end-cleaving exocellulases in polysaccharide chain depolymerization. How endo–exo synergy becomes effective in the deconstruction of a solid substrate that presents cellulose chains assembled into crystalline material is an open question of the mechanism, with immediate implications on the bioconversion efficiency of cellulases. Here, based on single-molecule evidence from real-time atomic force microscopy, we discover that endo- and exocellulases engage in the formation of transient clusters of typically three to four enzymes at the cellulose surface. The clusters form specifically at regular domains of crystalline cellulose microfibrils that feature molecular defects in the polysaccharide chain organization. The dynamics of cluster formation correlates with substrate degradation through a multilayer-processive mode of chain depolymerization, overall leading to the directed ablation of single microfibrils from the cellulose surface. Each multilayer-processive step involves the spatiotemporally coordinated and mechanistically concerted activity of the endo- and exocellulases in close proximity. Mechanistically, the cooperativity with the endocellulase enables the exocellulase to pass through its processive cycles ∼100-fold faster than when acting alone. Our results suggest an advanced paradigm of efficient multienzymatic degradation of structurally organized polymer materials by endo–exo synergetic chain depolymerization.

producer of cellases (2). T. reesei was grown on wheat straw and harvested as described in our previous study. (3) In brief, the supernatant was clarified via centrifugation (4420 g at 4 °C) for 20 min through a glass microfiber filter (Whatman). The purified cellulase mixture was supplemented with 0.05% w/v sodium azide and stored at 4 °C. SDS PAGE analysis (data not shown) confirmed the cellulase composition known from literature (4).

Preparation of isolated cellulases Cel7A and Cel7B
The major fungal exocellulase Cel7A was isolated from the T. reesei supernatant, as described earlier. (5) In brief, the buffer of the complete system was exchanged to 20 mM triethanolamine with pH 7.0 using centrifugal concentrators (10 kDa, Vivaspin Turbo, Sartorius). Purification was executed using an ÄKTA FPLC (Amersham Biosciences) where the mixture was loaded onto a 6 mL pre-packed column (Resource Q, GE Healthcare) equilibrated with the same buffer. The purification was performed with a linear gradient of 0 -300 mM sodium chloride over 10 column volumes. Only the desired Cel7A elutes at about 180 mM sodium chloride, which facilitates precise collection of the enzyme. The buffer of the isolated Cel7A was exchanged to 50 mM sodium acetate with pH 5.0 and it was stored at 4 °C. SDS PAGE analysis (data not shown) showed a single protein band approaching 65 kDa in accordance with the molecular size of the enzyme.
The major endocellulase Cel7B was commercially obtained from Megazyme. Note, that Cel7B originates from Trichoderma longibrachiatum, which is an anamorph of T. reesei. It is almost identical to its counterpart from T. reesei regarding their amino acid sequence (identity: 95%, similarity: > 95%). Cel7B was stored as provided at 4 °C. Prior to experimental use the buffer was exchanged to 50 mM sodium acetate buffer, pH 5.0, using disposable centrifugal concentrators with 100 kDa mass cut-off filters (Vivaspin Turbo 15, Sartorius).
Protein concentration of the complete cellulase mixture was measured using a commercially available kit (Pierce BCA Protein Assay Kit, Thermo scientific). Concentrations of the isolated enzymes were measured with UV absorbance at a wavelength of 280 nm, using their respective molar extinction coefficients of 86,760 M -1 cm -1 for Cel7A and 74,940 M -1 cm -1 for Cel7B. (1)

Atomic force microscopy -In situ degradation experiments
In situ observation of cellulose degradation by the cellulases was done with a commercially available atomic force microscope (AFM, Dimension Fast Scan Bio, Bruker) equipped with a commercially available controller (Nanoscope V, Bruker) and software (Nanoscope 9.2, Bruker). Measurements were performed in liquid environment using tapping mode with FastScan DSS probes (Bruker) as described in our resent work. (1) The liquid environment was heated with a temperature controller (Bruker) to 35 °C. Single cellulose fibers were immobilized on highly oriented pyrolytic graphite crystals (HOPG, Grade I, SPI supplies) by incubating the freshly cleaved surface with 300 µL of single cellulose fiber suspension for 15 min. The surface was rinsed with deionized water. Residual droplets were removed by spraying carbon dioxide for 2-3 s. The HOPG crystal was mounted onto the AFM stage. To create the liquid environment, 250 µL of sodium acetate buffer was pipetted on the crystals surface. The scan head was gently immersed into the buffer drop. In the heated liquid environment, the probe needed to equilibrate for 10 min prior to measurement to ensure good detector alignment and stable measurement conditions. Engagement was performed automatically using the "smart engage" setting. After a stable contact between tip and surface was ensured, 30 µm  30 µm areas were systematically scanned for isolated, firmly attached fibers. As soon as a suitable fiber was detected, 10-100 µL of the relevant enzyme preparations were carefully injected in 10 µL portions.
The experiments using Cel7A at elevated protein concentration were performed by adding 30 µL and 100 µL of a more concentrated stock (2000 µg/mL).
The deconstruction was observed at least for 2 h or until the fiber was completely gone. During long measurement times (> 3 h) it was necessary to inject additional buffer due to evaporation.

Atomic force microscopy -Device parameters during measurement
As described in full detail in our previous work (1), AFM parameters were selected and adapted with cautious sensitivity based on the feedback from the device. Summarily, topography, phase and amplitude images were measured for every frame. The Drive Amplitude was adjusted by observing the quality of the phase image, the Amplitude Setpoint was regulated to just below contact (~70-90% of free amplitude). Usually, the same Setpoint was used during a measurement, however, in case of any drift of the z-piezo, it was adjusted accordingly. The Integral Gain was continuously set just below resonance, the Proportional Gain was kept at a value about 3-5 times of the Integral Gain value. The Z-range of the scanner was decreased by half its maximum value.
In contrast to our earlier work, the main objective was to illuminate enzyme behavior during degradation rather than degradation patterns visible on the cellulose substrate. For this, higher time resolution was necessary. Switching from globally observed fibers to locally acting enzymes allowed for smaller scan areas (0.0500 µm 2 < scan area < 0.0025 µm 2 ), hence faster measurements were possible. The scan rate was increased until the image quality could not be improved any further just before the point of obscuring the enzymes due to measurement artefacts. To exhaust the maximal possible scan speed, the amount of measurement points was reduced by choosing larger pixel step sizes. This method enabled measurement speeds of up to 2 frames/s.

Application of Cel7A as single enzyme at different concentrations.
Isolated Cel7A was investigated with respect to cluster formation. Experiments were performed by adding 20 µL of Cel7A at a concentration of either 15 µg/mL or 2000 µg/mL to our standard AFM setup (see Atomic force microscopy -In situ degradation experiments). At the lower concentration, Cel7A was observed on the cellulose surface continuously adsorbing on fibril defect locations or endings (Fig. S2a). Degradation occurred along the longitudinal axis of the fibrils, resulting in continuous ablation (Fig. S2b). Interestingly, the constant fiber loss decelerated after 40 min and 8 % deconstruction. No cluster was observed throughout the measurement.
The use of Cel7A at the higher concertation led to a rapid disappearance of the fiber (after ~34 min), however, the deconstruction process was clearly different from that of the complete cellulase system as well as the Cel7A/Cel7B mixture (Fig. S3, Movie S3). A large number of 6 Cel7A molecules were observed to absorb, aggregate and slide longitudinally along the fiber surface. The deconstructions occurred in an ablation style, while discrete steps of multilayered fibril removal were absent. Note that a major part of the fiber was destabilized and removed by the AFM tip.
Preparation of Cel7B-treated cellulose fibers and their subsequent degradation by Cel7A.
Bacterial cellulose fibers were pretreated with Cel7B as follows. Five mL of a single fiber suspension (< 0.1 mg/L) were adjusted to pH 5.0 by adding 1 mL of 300 mM sodium acetate buffer (pH 5.0). Subsequently, 1 mL of this suspension was transferred to a Thermomixer comfort (Eppendorf) and heated to 35 °C, which corresponded to the conditions in the AFM experiments. 20 µL Cel7B (15 µg/mL) were added to the reaction volume. After 1 h, the reaction was stopped by adding 100 µL of 1 M sodium hydroxide. The suspension was stored at 4 °C and used within 24 hours. The pretreated fibers were used as substrate for Cel7A in the AFM measurement, which was performed similarly as described in Atomic force microscopy -In situ degradation experiments. The effect of Cel7A was observed continuously on the cellulose surface (Fig. S5a), and after 2 h, 4 % of the total fiber volume was removed by ablation and thinning (Fig. S5b). Interestingly, the deconstruction decelerated after 50 min. The same behavior was observed for Cel7A acing on untreated cellulose fibers. No formation of clusters was observed.

AFM analysis -Cluster size, speed and degradation
The size of the clusters was assessed with common tools in Gwyddion (Version 2.60). (6) In more detail, the cluster was manually masked by an operator and the projected area of said mask was determined with the "measure individual grains" tool in nm 2 . To account for uncertainties regarding the exact edges of the flexible clusters, two different operators independently assigned masks to the same clusters, resulting in an average difference of ~20%, being ~50 nm 2 (Fig S6a, b). This procedure was also performed for the isolated exocellulase Cel7A to obtain a reference value for single enzymes ( Fig. 4a).
However, enzymes within clusters do not necessarily assemble in a linear fashion on a plane surface but rather on a curved surface and, thus, the projected area of a cluster does not scale linearly with the enzyme amount. Consequentially, the number of enzymes involved in a cluster cannot readily be accessed by dividing the projected cluster area by the projected area of a single enzyme. Therefore, different configurations of enzymes on a cellulose fibril were measured in silico with the BioAFMViewer (7) and manually compared to the actually measured AFM height images by two independent operators for a more accurate estimation.
For this it was necessary to generate PDB files resembling different enzyme scenarios, e.g., one Cel7A and one Cel7B on the edge of a microfibril (Fig. S7a, 1:1). The files were generated using Pymol (Version 4.6.0). Structure information for Cel7A and Cel7B were taken from the Protein Data Bank website (https://www.rcsb.org/) with file IDs 4d5q and 1eg1, respectively. Note, that these structures only resemble the catalytic core of the enzymes, without linker or binding module and therefore will appear smaller in the in-silico measurement compared to the actual AFM measurement. Structure information for the microfibril was generated using the cellulose builder. Cel7A/Cel7B mixtures were as follows: 1 Cel7A + 0 Cel7B, 1 Cel7A + 1Cel7B, 2 Cel7A + 1 Cel7B and 3 Cel7B (Fig. S7a). Note, that the arrangement in Pymol happened only considering the geometry of the components and therefore disregarding any physical or chemical surface interactions. However, this does not limit our use case, as we only employ these created scenarios as visual aid for the estimation of enzyme number. These scenarios were exported as PDB files and put into the BioAFMViewer for in silico measurements. Settings were chosen to mimic the geometry of the tip used in the experiments (FastScan DSS): Scan step: 1 nm, cone angle: 15. The tip radius was varied between 1 nm (Fig. S7b), 2 nm (Fig. S7c) and 3 nm (Fig. S7d) to prevent over-counting caused by a broad tip. The scan area was set to 150 x 100 nm. Cutting the actually measured cluster frames to the same dimensions enabled a side-by-side comparison between the appearance of the in situ and in silico measured clusters. Again, with this comparison method, two different operators evaluated the enzyme count within the clusters, which led to an average difference of 0.7 enzymes (Fig. S8).
Analysis regarding the volumetric degradation behavior was performed as described previously. (1) Briefly, for every AFM frame within one sequence, the sum of all height pixel entries was calculated ("fiber volume"). As the fiber was continuously deconstructed, also the overall sum of height pixel decreased. The first fiber volume was defined as 100% and by successively subtracting the fiber volume of the consecutively following frames the loss of fiber height can be tracked throughout the measurement.

Calculation of the processive turnover rate of cellulase clusters
To calculate the efficiency of the multienzyme clusters as a whole, the number of cellulose chains within the degraded fibril had to be estimated. An often used and widely accepted geometrical representation of bacterial cellulose microfibrils is that of a ribbon with a rectangular cross-section (9,10). However, the reported dimensions and aspect ratios of the cross-sectional area of these ribbonshaped fibrils vary by a factor of up to 4 between different studies (9,11). Hence, in this study, the fibril was assumed to have a cylindrical shape with a consequently circular cross section. Assuming that the cross-section of cellobiose is ~0.4 nm 2 (see ref. (12) for details), it was calculated how many cellobiose units could theoretically fit into fibrils with various diameters (Fig. S9, adapted from ref. (13)). The fibril diameters in our fibrils were measured directly as the fibril height in the AFM experiments. Fibril heights ranged from 2 to 4 nm (Fig. S9, highlighted in grey) representing about 8 to 36 cellulose chains. On average, the fibril height was 3.5 nm, hence containing 24 chains (Fig. S9, green x symbol). This estimate was further validated by measuring the 36 chain model of the fibril in silico using the BioAFMViewer. (7) The cellulose file was generated using the cellulose builder (8) as described above. The model of the fibril (Fig. S10a) was measured with the AFMBioViewer with settings resembling the actual experiments (scan step: 1 nm, cone angle: 15 °, tip radius: 2 nm) (Fig.   S10b). Analysis of the horizontal and vertical profiles of the simulated fibril indicated a theoretical height of 4 nm for 36 chains (Fig. S10c). Regarding the experimentally observed height of ~3.5 nm, our estimation with 24 chains was taken as prototypical fibril.
Combining the assumed number of chains, , in one microfibril, the reported length of a cellobiose unit (12), , the average cluster velocity, , and the average number of present enzymes, , led to calculation of the turnover rate ( ). Identification and separation of objects (cellulose fibers and enzymes) and background (HOPG) were done by identifying edges and surfaces with user set gradient and median parameters. Both user defined parameters were set regarding only the first image of a sequence. Correct settings of the parameters were fundamental for a high-quality processing of the data. Therefore, they were optimized until >90% of the pixels were accurately assigned either to object or background. Once suitable parameters were found, they were not changed within the sequence, resulting in individual masks for every frame within one sequence calculated with the same settings.

AFM image processing and video construction
Assigning every image frame with its mask allowed for automated data manipulation to correct the raw data from measurement artifacts in an efficient way. This was especially important for measurement sequences consisting of over hundreds of frames. Furthermore, time dependent analysis, regarding the change of the object (in our case cellulose deconstruction), was made possible.
The first manipulation step corrected for tilts in the images. This was done by calculating a plane that fitted best to the 3D data points given by all pixels defined as background in the masking step. The distances (perpendicular to the plane) between all points and the plane were minimized using least square method. The resulting plane was subtracted from the whole image (background + object pixels), thereby correcting the tilt.
The second step corrected for mismatched baselines of rows in fast scan direction, by fitting a polynomial of degree 1 to the background pixels with least square method. The fitted lines were successively subtracted from all pixels in the individual rows. Rows consisting of > 80% object pixels were not corrected as the small amount of background pixels often were not enough input for successful fitting but would rather introduce calculation artifacts.
Consistent false-color scaling was performed by setting the lowest value for every image to zero. Once all frames were set to zero, one user chosen maximum value was defined. All images were scaled to this one maximum value.
24-bit depth) for movie generation as well as to 2D matrices containing the, now corrected, actual height information for further analysis.
Phase and amplitude data were not manipulated and solely processed regarding the false-color scale.
For the phase channel, the lowest value was set to zero and, similar to the topography channel, one maximum value was chosen to scale all the images to. For the amplitude channel, this routine alone was not sufficient due to controller feedback errors and an asymmetric distribution of amplitude values. Both problems cause affected images to be scaled in the upper false-color region, rendering interesting features hardly visible for the human eye. This was counteracted by defining the minimum value of the image newly by mirroring the range between maximum and mean value of all pixels, as described in our earlier work. (1) The last step of preprocessing was drift correction of the image sequence. It was performed similarly for all the channels also in MATLAB as an adaptation from Sugar et al (14). In brief, a routine was created, were a user-chosen reference image is compared to all other images in the sequence. For every image it is determined how many pixels in x-and y-directions it has to be shifted to best match the reference image. To find the best match, and the number of steps to it, the cross-correlation function between both images was calculated.    Scaling is varying for different data channels: Height and phase channel images within a sequence are scaled to a user set maximum, whereas amplitude channel data is automatically processed to improve visibility of object features (Scaling). The drift between preprocessed images within a sequence is corrected using the cross-correlation method (Drift correction).
The resulting image sequence can be used for further calculations and video generation (Analysis). Image acquisition rate and resolution were 5 frames/min and 1 nm/pixel, respectively. Scale bar, time stamps and false color scale are included in the video.